ORIGINAL ARTICLE Development of analytical methods for NMR spectra and application to a 13 C toxicology study Gary L. Jahns Æ Michael N. Kent Æ Lyle D. Burgoon Æ Nicholas DelRaso Æ Timothy R. Zacharewski Æ Nicholas V. Reo Received: 13 May 2008 / Accepted: 1 December 2008 / Published online: 19 December 2008 Ó Springer Science+Business Media, LLC 2008 Abstract Metabolomics offers the potential to assess the effects of toxicants on metabolite levels. To fully realize this potential, a robust analytical workflow for identifying and quantifying treatment-elicited changes in metabolite levels by nuclear magnetic resonance (NMR) spectrometry has been developed that isolates and aligns spectral regions across treatment and vehicle groups to facilitate analytical comparisons. The method excludes noise regions from the resulting reduced spectra, significantly reducing data size. Principal components analysis (PCA) identifies data clus- ters associated with experimental parameters. Cluster- centroid scores, derived from the principal components that separate treatment from vehicle samples, are used to reconstruct the mean spectral estimates for each treatment and vehicle group. Peak amplitudes are determined by scanning the reconstructed mean spectral estimates. Con- fidence levels from Mann–Whitney order statistics and amplitude change ratios are used to identify treatment- related changes in peak amplitudes. As a demonstration of the method, analysis of 13 C NMR data from hepatic lipid extracts of immature, ovariectomized C57BL/6 mice trea- ted with 30 lg/kg 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) or sesame oil vehicle, sacrificed at 72, 120, or 168 h, identified 152 salient peaks. PCA clustering showed a prominent treatment effect at all three time points stud- ied, and very little difference between time points of treated animals. Phenotypic differences between two ani- mal cohorts were also observed. Based on spectral peak identification, hepatic lipid extracts from treated animals exhibited redistribution of unsaturated fatty acids, choles- terols, and triacylglycerols. This method identified significant changes in peaks without the loss of information associated with spectral binning, increasing the likelihood of identifying treatment-elicited metabolite changes. Keywords 13 C NMR spectra  Metabolite identification  Chemical shift  Principal components analysis  Cross-correlation  Order statistics 1 Introduction In response to increasing drug development costs due to candidate safety and efficacy failures during clinical trails (Dimasi 2001; FDA 2004), pharmaceutical companies are exploring toxicogenomic technologies, such as metabolo- mics, to identify safety issues early in the development process using biomarkers that can monitor toxicity (Robertson 2005). The strengths of metabolomics include the ability to monitor thousands of metabolites in a single sample, to perform metabolite screening from non-invasive sample collections, to profile changes across dose and time, G. L. Jahns (&) BAE Systems Advanced Information Technologies, San Diego, CA 92127, USA e-mail: gary.jahns@baesystems.com M. N. Kent  N. V. Reo Department of Biochemistry & Molecular Biology, Boonshoft School of Medicine, Wright State University, Dayton, OH 45429, USA L. D. Burgoon  T. R. Zacharewski Department of Biochemistry & Molecular Biology, National Food Safety & Toxicology Center, Center for Integrative Toxicology, Michigan State University, East Lansing, MI 48824, USA N. DelRaso Human Effectiveness Directorate, Air Force Research Laboratory, Wright Patterson Air Force Base, Dayton, OH 45433, USA 123 Metabolomics (2009) 5:253–262 DOI 10.1007/s11306-008-0148-9